Detection defense against adversarial attacks with saliency map. Issue 12 (8th May 2021)

• Record Type: Journal Article
• Authors: Ye, Dengpan; Chen, Chuanxi; Liu, Changrui; Wang, Hao; Jiang, Shunzhi
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 10193
• Page End: 10210
• Publication Date: 2021-05-08
• Subjects: adversarial defense -- adversarial example -- deep neural network -- machine learning -- saliency map; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22458
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision and can cause the deep models misbehave. Such phenomenon may lead to severely inestimable consequences in the safety and security critical applications. Existing defenses are trend to harden the robustness of models against adversarial attacks, for example, adversarial training technology. However, these are usually intractable to implement due to the high cost of retraining and the cumbersome operations of altering the model architecture or parameters. In this paper, we discuss the saliency map method from the view of enhancing model interpretability, it is similar to introducing the mechanism of the attention to the model, so as to comprehend the progress of object identification by the deep networks. We then propose a novel method combined with additional noises and utilize the inconsistency strategy to detect adversarial examples. Our experimental results of some representative adversarial attacks on common data sets including ImageNet and popular models show that our method can detect all the attacks with high detection success rate effectively. We compare it with the existing state‐of‐the‐art technique, and the experiments indicate that our method is more general.